文献类型：期刊文献

中文题名：非结构化环境下适用多类型激光雷达的地面无人车辆负障碍物检测

英文题名：Negative Obstacle Detection for Ground Unmanned Vehicles Using Multiple Types of LiDAR in Unstructured Environments

作者：武丹凤[1,2];陈同舟[3];匡敏驰[4];宋春森[5];周芬芬[1,2];张学艳[1,2]

第一作者：武丹凤

机构：[1]北京联合大学北京市信息服务工程重点实验室,北京100101;[2]北京联合大学机器人学院,北京100027;[3]桂林电子科技大学机电工程学院,广西桂林541004;[4]清华大学精密仪器系,北京100084;[5]清华大学天津高端装备研究院,天津300300

第一机构：北京联合大学北京市信息服务工程重点实验室

年份：2025

卷号：46

期号：11

起止页码：130-146

中文期刊名：兵工学报

外文期刊名：Acta Armamentarii

收录：;北大核心:【北大核心2023】；

基金：时空信息精密感知技术全国重点实验室开放基金资助项目(STL2023-B-06-01(K));北京联合大学科研项目资助项目(ZK20202201)。

语种：中文

中文关键词：地面无人车辆;激光雷达;负障碍物;非结构化环境

外文关键词：unmanned ground vehicle;liDAR;negative obstacle;unstructured environment

摘要：地面无人车辆在非结构化环境中行驶时,负障碍物对车辆行驶安全构成了重大威胁。然而,现有的负障碍物检测研究中,基于相机的检测方法不能良好适用于光照条件差、背景复杂的非结构化环境,而基于激光雷达的负障碍物检测方法大多从机械式激光雷达线束特性出发进行设计,通用性差。为此,提出了一种兼容性强且具备良好扩展性的负障碍物检测方法,能够适应不同类型的激光雷达,并实现高精度的负障碍物检测。方法针对点云数据进行预处理,提取地面感兴趣区域并进行点云的姿态矫正;采用自适应分辨率极坐标栅格化技术,增强点云数据的空间表示能力;设计了负障碍物栅格特征描述子,结合点云的空洞特性、高度差异和最小高度等多个特征,提取潜在的负障碍物区域;引入多帧融合策略,通过地图重投影和基于贝叶斯规则的概率更新,最终输出高精度的负障碍物表面占据范围。实验结果表明,所提出的方法同时适用于不同扫描方式的激光雷达,能够在复杂非结构化环境中有效识别负障碍区域,具有良好的通用性与检测精度。
The negative obstacles pose a significant threat to the driving safety of unmanned ground vehicles(UGVs)when they operate in unstructured environments.However,in the existing research on negative obstacle detection,the camera-based detection methods are not well suited for the detection of negative obstacles in unstructured environments with poor lighting conditions and complex backgrounds;and LiDAR(light detection and ranging)-based detection methods are mostly designed based on the characteristics of mechanical LiDAR harnesses and have poor universality.Therefore,a highly compatible and scalable negative obstacle detection method is proposed,which can adapt to different types of LiDAR and achieve high-precision negative obstacle detection.The proposed method involves preprocessing the point cloud data,extracts the ground regions of interest,and performs point cloud pose correction.The adaptive resolution polar coordinate rasterization technology is used to enhance the spatial representation capability of point cloud data.A negative obstacle grid feature descriptor is designed,and the potential negative obstacle regions are extracted from multiple features such as the hollow characteristics,height differences,and minimum height of point clouds.A multi-frame fusion strategy is introduced,and the high-precision negative obstacle surface occupancy range is outputed through map reprojection and Bayesian rule-based probability updates.The experimental results show that the proposed method is applicable to LiDARs with different scanning modes,and can effectively identify the negative obstacle areas in complex unstructured environments.